For single-policy datasets, i.e., approval-only datasets and approval-rejection datasets, the following approaches are compared.
Baselines consists of three commonly-used classifiers for credit scoring: LR, MLP and XGB.
Among semi-supervised approaches, Self-Training (ST) [1]} is incorporated with LR, MLP and XGB.
Another typical semi-supervised reject inference approach SS-GMM [2]} is also compared.
For counterfactual approaches, we involve IPS [3]}[4]}, DR [5]}, DRJL [6]}, ACL [7]} and SRDO [8]}, and incorporate them with LR and MLP.
For multi-task approaches, besides our proposed RMT-Net, we involve Cross-Stitch [9]}, MMOE [10]} and PLE [11]}.
